Overview

Dataset statistics

Number of variables44
Number of observations20000
Missing cells286735
Missing cells (%)32.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.7 MiB
Average record size in memory352.0 B

Variable types

Numeric39
Categorical5

Alerts

SBP is highly correlated with MAP and 1 other fieldsHigh correlation
MAP is highly correlated with SBP and 1 other fieldsHigh correlation
DBP is highly correlated with SBP and 1 other fieldsHigh correlation
BaseExcess is highly correlated with HCO3 and 1 other fieldsHigh correlation
HCO3 is highly correlated with BaseExcess and 1 other fieldsHigh correlation
pH is highly correlated with BaseExcess and 1 other fieldsHigh correlation
BUN is highly correlated with CreatinineHigh correlation
Creatinine is highly correlated with BUNHigh correlation
Bilirubin_direct is highly correlated with Bilirubin_totalHigh correlation
Bilirubin_total is highly correlated with Bilirubin_directHigh correlation
Hct is highly correlated with HgbHigh correlation
Hgb is highly correlated with HctHigh correlation
Unit1 is highly correlated with Unit2High correlation
Unit2 is highly correlated with Unit1High correlation
SBP is highly correlated with MAP and 1 other fieldsHigh correlation
MAP is highly correlated with SBP and 1 other fieldsHigh correlation
DBP is highly correlated with SBP and 1 other fieldsHigh correlation
BaseExcess is highly correlated with HCO3 and 1 other fieldsHigh correlation
HCO3 is highly correlated with BaseExcess and 1 other fieldsHigh correlation
pH is highly correlated with BaseExcess and 1 other fieldsHigh correlation
BUN is highly correlated with Creatinine and 1 other fieldsHigh correlation
Creatinine is highly correlated with BUN and 1 other fieldsHigh correlation
Bilirubin_direct is highly correlated with Bilirubin_totalHigh correlation
Phosphate is highly correlated with BUN and 1 other fieldsHigh correlation
Bilirubin_total is highly correlated with Bilirubin_directHigh correlation
Hct is highly correlated with HgbHigh correlation
Hgb is highly correlated with HctHigh correlation
Unit1 is highly correlated with Unit2High correlation
Unit2 is highly correlated with Unit1High correlation
SBP is highly correlated with MAPHigh correlation
MAP is highly correlated with SBP and 1 other fieldsHigh correlation
DBP is highly correlated with MAPHigh correlation
BaseExcess is highly correlated with HCO3High correlation
HCO3 is highly correlated with BaseExcessHigh correlation
BUN is highly correlated with CreatinineHigh correlation
Creatinine is highly correlated with BUNHigh correlation
Bilirubin_direct is highly correlated with Bilirubin_totalHigh correlation
Bilirubin_total is highly correlated with Bilirubin_directHigh correlation
Hct is highly correlated with HgbHigh correlation
Hgb is highly correlated with HctHigh correlation
Unit1 is highly correlated with Unit2High correlation
Unit2 is highly correlated with Unit1High correlation
Unit2 is highly correlated with Unit1High correlation
Unit1 is highly correlated with Unit2High correlation
EtCO2 has 16784 (83.9%) missing values Missing
BaseExcess has 19442 (97.2%) missing values Missing
HCO3 has 19584 (97.9%) missing values Missing
FiO2 has 14178 (70.9%) missing values Missing
pH has 14246 (71.2%) missing values Missing
PaCO2 has 14221 (71.1%) missing values Missing
SaO2 has 14875 (74.4%) missing values Missing
AST has 11536 (57.7%) missing values Missing
BUN has 1591 (8.0%) missing values Missing
Alkalinephos has 11530 (57.6%) missing values Missing
Calcium has 1550 (7.8%) missing values Missing
Chloride has 18383 (91.9%) missing values Missing
Creatinine has 1588 (7.9%) missing values Missing
Bilirubin_direct has 18529 (92.6%) missing values Missing
Glucose has 1173 (5.9%) missing values Missing
Lactate has 15240 (76.2%) missing values Missing
Magnesium has 3543 (17.7%) missing values Missing
Phosphate has 8365 (41.8%) missing values Missing
Potassium has 1434 (7.2%) missing values Missing
Bilirubin_total has 11522 (57.6%) missing values Missing
TroponinI has 13436 (67.2%) missing values Missing
Hct has 1953 (9.8%) missing values Missing
Hgb has 1941 (9.7%) missing values Missing
PTT has 15602 (78.0%) missing values Missing
WBC has 2000 (10.0%) missing values Missing
Fibrinogen has 18052 (90.3%) missing values Missing
Platelets has 1992 (10.0%) missing values Missing
Unit1 has 6095 (30.5%) missing values Missing
Unit2 has 6095 (30.5%) missing values Missing
FiO2 is highly skewed (γ1 = -50.73903075) Skewed
PatientID is uniformly distributed Uniform
PatientID has unique values Unique
HospAdmTime has 1145 (5.7%) zeros Zeros

Reproduction

Analysis started2021-11-29 10:24:57.310969
Analysis finished2021-11-29 10:25:07.140818
Duration9.83 seconds
Software versionpandas-profiling v3.1.1
Download configurationconfig.json

Variables

PatientID
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct20000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110000.5
Minimum100001
Maximum120000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:07.193099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum100001
5-th percentile101000.95
Q1105000.75
median110000.5
Q3115000.25
95-th percentile119000.05
Maximum120000
Range19999
Interquartile range (IQR)9999.5

Descriptive statistics

Standard deviation5773.647028
Coefficient of variation (CV)0.05248746167
Kurtosis-1.2
Mean110000.5
Median Absolute Deviation (MAD)5000
Skewness0
Sum2200010000
Variance33335000
MonotonicityStrictly increasing
2021-11-29T11:25:07.308984image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000011
 
< 0.1%
1133311
 
< 0.1%
1133381
 
< 0.1%
1133371
 
< 0.1%
1133361
 
< 0.1%
1133351
 
< 0.1%
1133341
 
< 0.1%
1133331
 
< 0.1%
1133321
 
< 0.1%
1133301
 
< 0.1%
Other values (19990)19990
> 99.9%
ValueCountFrequency (%)
1000011
< 0.1%
1000021
< 0.1%
1000031
< 0.1%
1000041
< 0.1%
1000051
< 0.1%
1000061
< 0.1%
1000071
< 0.1%
1000081
< 0.1%
1000091
< 0.1%
1000101
< 0.1%
ValueCountFrequency (%)
1200001
< 0.1%
1199991
< 0.1%
1199981
< 0.1%
1199971
< 0.1%
1199961
< 0.1%
1199951
< 0.1%
1199941
< 0.1%
1199931
< 0.1%
1199921
< 0.1%
1199911
< 0.1%

HR
Real number (ℝ≥0)

Distinct197
Distinct (%)1.0%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean69.45094019
Minimum20
Maximum154.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:07.429010image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile48
Q160
median68
Q378
95-th percentile94
Maximum154.5
Range134.5
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.23210391
Coefficient of variation (CV)0.2049231281
Kurtosis0.5554990009
Mean69.45094019
Median Absolute Deviation (MAD)9
Skewness0.4629014619
Sum1388741
Variance202.5527818
MonotonicityNot monotonic
2021-11-29T11:25:07.543502image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60823
 
4.1%
68676
 
3.4%
62672
 
3.4%
70643
 
3.2%
66606
 
3.0%
64592
 
3.0%
58561
 
2.8%
80535
 
2.7%
74529
 
2.6%
72522
 
2.6%
Other values (187)13837
69.2%
ValueCountFrequency (%)
203
< 0.1%
212
< 0.1%
222
< 0.1%
231
 
< 0.1%
23.51
 
< 0.1%
241
 
< 0.1%
252
< 0.1%
262
< 0.1%
26.51
 
< 0.1%
272
< 0.1%
ValueCountFrequency (%)
154.51
 
< 0.1%
1461
 
< 0.1%
1361
 
< 0.1%
1341
 
< 0.1%
133.51
 
< 0.1%
1331
 
< 0.1%
132.51
 
< 0.1%
1322
< 0.1%
1303
< 0.1%
128.51
 
< 0.1%

O2Sat
Real number (ℝ≥0)

Distinct125
Distinct (%)0.6%
Missing6
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean91.8326748
Minimum20
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:07.658296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile82.5
Q191
median93
Q395
95-th percentile98
Maximum100
Range80
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.728730914
Coefficient of variation (CV)0.07327164246
Kurtosis34.74170797
Mean91.8326748
Median Absolute Deviation (MAD)2
Skewness-4.726614861
Sum1836102.5
Variance45.27581971
MonotonicityNot monotonic
2021-11-29T11:25:07.763478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
942162
10.8%
932161
10.8%
922152
10.8%
952018
10.1%
961666
 
8.3%
911616
 
8.1%
901372
 
6.9%
971183
 
5.9%
89650
 
3.2%
98639
 
3.2%
Other values (115)4375
21.9%
ValueCountFrequency (%)
2012
0.1%
214
 
< 0.1%
224
 
< 0.1%
233
 
< 0.1%
246
< 0.1%
263
 
< 0.1%
271
 
< 0.1%
282
 
< 0.1%
293
 
< 0.1%
303
 
< 0.1%
ValueCountFrequency (%)
100183
 
0.9%
99.526
 
0.1%
99323
 
1.6%
98.538
 
0.2%
98639
 
3.2%
97.577
 
0.4%
971183
5.9%
96.598
 
0.5%
961666
8.3%
95.5119
 
0.6%

Temp
Real number (ℝ≥0)

Distinct136
Distinct (%)0.7%
Missing49
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean36.0829808
Minimum30
Maximum39.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:07.874486image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile35.15
Q135.8
median36.1
Q336.5
95-th percentile37
Maximum39.2
Range9.2
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.6146643085
Coefficient of variation (CV)0.01703474311
Kurtosis9.011881438
Mean36.0829808
Median Absolute Deviation (MAD)0.3
Skewness-1.324003618
Sum719891.55
Variance0.3778122121
MonotonicityNot monotonic
2021-11-29T11:25:07.980259image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
362163
 
10.8%
36.41415
 
7.1%
36.21399
 
7.0%
36.51320
 
6.6%
36.31304
 
6.5%
36.11258
 
6.3%
35.91142
 
5.7%
35.81039
 
5.2%
36.6961
 
4.8%
35.6920
 
4.6%
Other values (126)7030
35.1%
ValueCountFrequency (%)
303
< 0.1%
30.11
 
< 0.1%
30.41
 
< 0.1%
30.52
< 0.1%
30.61
 
< 0.1%
30.81
 
< 0.1%
30.92
< 0.1%
31.21
 
< 0.1%
31.251
 
< 0.1%
31.31
 
< 0.1%
ValueCountFrequency (%)
39.21
 
< 0.1%
39.11
 
< 0.1%
38.83
< 0.1%
38.73
< 0.1%
38.61
 
< 0.1%
38.52
 
< 0.1%
38.43
< 0.1%
38.35
< 0.1%
38.24
< 0.1%
38.17
< 0.1%

SBP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct272
Distinct (%)1.4%
Missing24
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean98.92293402
Minimum20
Maximum183
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:08.086580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile73
Q187
median97
Q3110
95-th percentile131
Maximum183
Range163
Interquartile range (IQR)23

Descriptive statistics

Standard deviation17.89705432
Coefficient of variation (CV)0.1809191619
Kurtosis0.8313712617
Mean98.92293402
Median Absolute Deviation (MAD)11
Skewness0.239846158
Sum1976084.53
Variance320.3045533
MonotonicityNot monotonic
2021-11-29T11:25:08.192477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90540
 
2.7%
92530
 
2.6%
94525
 
2.6%
96495
 
2.5%
91479
 
2.4%
100477
 
2.4%
98469
 
2.3%
102445
 
2.2%
93445
 
2.2%
86443
 
2.2%
Other values (262)15128
75.6%
ValueCountFrequency (%)
204
< 0.1%
212
 
< 0.1%
225
< 0.1%
244
< 0.1%
254
< 0.1%
271
 
< 0.1%
281
 
< 0.1%
291
 
< 0.1%
304
< 0.1%
311
 
< 0.1%
ValueCountFrequency (%)
1831
 
< 0.1%
1711
 
< 0.1%
1681
 
< 0.1%
1673
< 0.1%
1661
 
< 0.1%
1655
< 0.1%
1644
< 0.1%
163.51
 
< 0.1%
1635
< 0.1%
1621
 
< 0.1%

MAP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct177
Distinct (%)0.9%
Missing102
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean68.7824907
Minimum30
Maximum140
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:08.310783image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile50
Q160
median67
Q376
95-th percentile92
Maximum140
Range110
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.80219302
Coefficient of variation (CV)0.1861257551
Kurtosis0.8476084219
Mean68.7824907
Median Absolute Deviation (MAD)8
Skewness0.4956551988
Sum1368634
Variance163.8961461
MonotonicityNot monotonic
2021-11-29T11:25:08.416497image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64775
 
3.9%
62761
 
3.8%
68699
 
3.5%
66680
 
3.4%
70670
 
3.4%
60657
 
3.3%
61644
 
3.2%
65641
 
3.2%
63632
 
3.2%
67613
 
3.1%
Other values (167)13126
65.6%
ValueCountFrequency (%)
3010
 
0.1%
3115
0.1%
3218
0.1%
3318
0.1%
349
 
< 0.1%
34.52
 
< 0.1%
3512
0.1%
35.51
 
< 0.1%
3627
0.1%
36.53
 
< 0.1%
ValueCountFrequency (%)
1401
< 0.1%
1361
< 0.1%
1301
< 0.1%
1292
< 0.1%
1261
< 0.1%
1251
< 0.1%
1241
< 0.1%
1231
< 0.1%
122.52
< 0.1%
1222
< 0.1%

DBP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct160
Distinct (%)0.8%
Missing27
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean51.84656737
Minimum20
Maximum109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:08.628655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile35
Q145
median51
Q358
95-th percentile70
Maximum109
Range89
Interquartile range (IQR)13

Descriptive statistics

Standard deviation10.49105356
Coefficient of variation (CV)0.2023480838
Kurtosis0.7413570725
Mean51.84656737
Median Absolute Deviation (MAD)6
Skewness0.2899518895
Sum1035531.49
Variance110.0622048
MonotonicityNot monotonic
2021-11-29T11:25:08.733426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
501044
 
5.2%
52923
 
4.6%
51864
 
4.3%
56776
 
3.9%
54775
 
3.9%
55767
 
3.8%
53767
 
3.8%
48759
 
3.8%
46695
 
3.5%
44645
 
3.2%
Other values (150)11958
59.8%
ValueCountFrequency (%)
2017
0.1%
20.53
 
< 0.1%
2115
0.1%
21.55
 
< 0.1%
2217
0.1%
2319
0.1%
23.52
 
< 0.1%
2431
0.2%
24.51
 
< 0.1%
2531
0.2%
ValueCountFrequency (%)
1091
 
< 0.1%
1051
 
< 0.1%
971
 
< 0.1%
962
< 0.1%
954
< 0.1%
942
< 0.1%
933
< 0.1%
92.51
 
< 0.1%
921
 
< 0.1%
91.51
 
< 0.1%

Resp
Real number (ℝ≥0)

Distinct59
Distinct (%)0.3%
Missing43
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean13.07446009
Minimum1
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:08.843426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q111
median13
Q315.5
95-th percentile18
Maximum35
Range34
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation3.497854282
Coefficient of variation (CV)0.2675333634
Kurtosis2.295771055
Mean13.07446009
Median Absolute Deviation (MAD)2
Skewness-0.4229964857
Sum260927
Variance12.23498458
MonotonicityNot monotonic
2021-11-29T11:25:08.949807image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
123400
17.0%
142642
13.2%
162519
12.6%
102141
10.7%
151641
8.2%
111408
7.0%
131387
6.9%
181036
 
5.2%
17614
 
3.1%
9487
 
2.4%
Other values (49)2682
13.4%
ValueCountFrequency (%)
1227
1.1%
1.518
 
0.1%
2140
0.7%
2.518
 
0.1%
359
 
0.3%
3.510
 
0.1%
444
 
0.2%
4.55
 
< 0.1%
553
 
0.3%
5.57
 
< 0.1%
ValueCountFrequency (%)
352
 
< 0.1%
342
 
< 0.1%
323
< 0.1%
312
 
< 0.1%
303
< 0.1%
293
< 0.1%
286
< 0.1%
27.51
 
< 0.1%
272
 
< 0.1%
26.51
 
< 0.1%

EtCO2
Real number (ℝ≥0)

MISSING

Distinct91
Distinct (%)2.8%
Missing16784
Missing (%)83.9%
Infinite0
Infinite (%)0.0%
Mean28.01912313
Minimum10
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:09.063613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile12
Q122
median28
Q333
95-th percentile39.5
Maximum100
Range90
Interquartile range (IQR)11

Descriptive statistics

Standard deviation11.00698314
Coefficient of variation (CV)0.3928382442
Kurtosis16.56850982
Mean28.01912313
Median Absolute Deviation (MAD)5.5
Skewness2.812296261
Sum90109.5
Variance121.1536777
MonotonicityNot monotonic
2021-11-29T11:25:09.168048image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30155
 
0.8%
28145
 
0.7%
32130
 
0.7%
24128
 
0.6%
29128
 
0.6%
26123
 
0.6%
31121
 
0.6%
27113
 
0.6%
34111
 
0.6%
25109
 
0.5%
Other values (81)1953
 
9.8%
(Missing)16784
83.9%
ValueCountFrequency (%)
1075
0.4%
10.513
 
0.1%
1131
0.2%
11.58
 
< 0.1%
1242
0.2%
12.59
 
< 0.1%
1340
0.2%
13.510
 
0.1%
1431
0.2%
14.511
 
0.1%
ValueCountFrequency (%)
1007
< 0.1%
994
< 0.1%
986
< 0.1%
979
< 0.1%
963
 
< 0.1%
951
 
< 0.1%
942
 
< 0.1%
932
 
< 0.1%
924
< 0.1%
861
 
< 0.1%

BaseExcess
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct206
Distinct (%)36.9%
Missing19442
Missing (%)97.2%
Infinite0
Infinite (%)0.0%
Mean-3.851164875
Minimum-23.2
Maximum9.1
Zeros1
Zeros (%)< 0.1%
Negative467
Negative (%)2.3%
Memory size156.4 KiB
2021-11-29T11:25:09.274718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-23.2
5-th percentile-11.1
Q1-5.975
median-3.7
Q3-1.3
95-th percentile3.015
Maximum9.1
Range32.3
Interquartile range (IQR)4.675

Descriptive statistics

Standard deviation4.310255972
Coefficient of variation (CV)-1.119208373
Kurtosis1.622403723
Mean-3.851164875
Median Absolute Deviation (MAD)2.4
Skewness-0.4472885894
Sum-2148.95
Variance18.57830654
MonotonicityNot monotonic
2021-11-29T11:25:09.376287image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4.411
 
0.1%
-3.910
 
0.1%
-4.59
 
< 0.1%
-38
 
< 0.1%
-1.78
 
< 0.1%
-2.68
 
< 0.1%
-2.58
 
< 0.1%
-3.78
 
< 0.1%
-2.48
 
< 0.1%
-2.88
 
< 0.1%
Other values (196)472
 
2.4%
(Missing)19442
97.2%
ValueCountFrequency (%)
-23.21
< 0.1%
-21.21
< 0.1%
-18.251
< 0.1%
-17.41
< 0.1%
-16.61
< 0.1%
-161
< 0.1%
-15.81
< 0.1%
-15.11
< 0.1%
-151
< 0.1%
-14.81
< 0.1%
ValueCountFrequency (%)
9.11
< 0.1%
91
< 0.1%
7.951
< 0.1%
7.62
< 0.1%
6.81
< 0.1%
6.31
< 0.1%
5.71
< 0.1%
5.51
< 0.1%
5.41
< 0.1%
5.11
< 0.1%

HCO3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct166
Distinct (%)39.9%
Missing19584
Missing (%)97.9%
Infinite0
Infinite (%)0.0%
Mean22.18509615
Minimum7.7
Maximum32.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:09.480859image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum7.7
5-th percentile16.9
Q120.4
median22.2
Q324.2125
95-th percentile27.9
Maximum32.4
Range24.7
Interquartile range (IQR)3.8125

Descriptive statistics

Standard deviation3.499780577
Coefficient of variation (CV)0.1577536808
Kurtosis1.392533606
Mean22.18509615
Median Absolute Deviation (MAD)2
Skewness-0.2309924944
Sum9229
Variance12.24846409
MonotonicityNot monotonic
2021-11-29T11:25:09.586082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.410
 
0.1%
229
 
< 0.1%
22.69
 
< 0.1%
24.89
 
< 0.1%
21.59
 
< 0.1%
23.58
 
< 0.1%
23.28
 
< 0.1%
21.38
 
< 0.1%
22.37
 
< 0.1%
20.77
 
< 0.1%
Other values (156)332
 
1.7%
(Missing)19584
97.9%
ValueCountFrequency (%)
7.71
< 0.1%
8.41
< 0.1%
12.451
< 0.1%
12.61
< 0.1%
13.11
< 0.1%
13.31
< 0.1%
13.61
< 0.1%
13.92
< 0.1%
14.12
< 0.1%
14.21
< 0.1%
ValueCountFrequency (%)
32.41
< 0.1%
322
< 0.1%
31.91
< 0.1%
30.91
< 0.1%
30.61
< 0.1%
30.11
< 0.1%
29.52
< 0.1%
29.42
< 0.1%
29.21
< 0.1%
29.12
< 0.1%

FiO2
Real number (ℝ)

MISSING
SKEWED

Distinct67
Distinct (%)1.2%
Missing14178
Missing (%)70.9%
Infinite0
Infinite (%)0.0%
Mean0.4056853315
Minimum-50
Maximum2
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size156.4 KiB
2021-11-29T11:25:09.691380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-50
5-th percentile0.21
Q10.3
median0.4
Q30.45
95-th percentile1
Maximum2
Range52
Interquartile range (IQR)0.15

Descriptive statistics

Standard deviation0.9536206482
Coefficient of variation (CV)2.350641185
Kurtosis2681.712423
Mean0.4056853315
Median Absolute Deviation (MAD)0.1
Skewness-50.73903075
Sum2361.9
Variance0.9093923408
MonotonicityNot monotonic
2021-11-29T11:25:09.799167image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.42241
 
11.2%
0.21719
 
3.6%
0.5693
 
3.5%
0.3378
 
1.9%
1328
 
1.6%
0.28294
 
1.5%
0.35202
 
1.0%
0.32156
 
0.8%
0.6145
 
0.7%
0.36119
 
0.6%
Other values (57)547
 
2.7%
(Missing)14178
70.9%
ValueCountFrequency (%)
-502
 
< 0.1%
0.011
 
< 0.1%
0.023
 
< 0.1%
0.031
 
< 0.1%
0.049
< 0.1%
0.059
< 0.1%
0.0613
0.1%
0.081
 
< 0.1%
0.11
 
< 0.1%
0.131
 
< 0.1%
ValueCountFrequency (%)
24
 
< 0.1%
1.21
 
< 0.1%
1328
1.6%
0.981
 
< 0.1%
0.953
 
< 0.1%
0.921
 
< 0.1%
0.910
 
0.1%
0.854
 
< 0.1%
0.841
 
< 0.1%
0.821
 
< 0.1%

pH
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct81
Distinct (%)1.4%
Missing14246
Missing (%)71.2%
Infinite0
Infinite (%)0.0%
Mean7.346866528
Minimum6.71
Maximum7.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:09.907759image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum6.71
5-th percentile7.2
Q17.3
median7.36
Q37.4
95-th percentile7.48
Maximum7.61
Range0.9
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.0906618608
Coefficient of variation (CV)0.01234020796
Kurtosis4.147079166
Mean7.346866528
Median Absolute Deviation (MAD)0.05
Skewness-1.128063207
Sum42273.87
Variance0.008219573004
MonotonicityNot monotonic
2021-11-29T11:25:10.010962image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.38350
 
1.8%
7.36343
 
1.7%
7.34321
 
1.6%
7.32304
 
1.5%
7.35277
 
1.4%
7.37277
 
1.4%
7.4276
 
1.4%
7.39256
 
1.3%
7.42242
 
1.2%
7.33238
 
1.2%
Other values (71)2870
 
14.3%
(Missing)14246
71.2%
ValueCountFrequency (%)
6.711
< 0.1%
6.721
< 0.1%
6.731
< 0.1%
6.781
< 0.1%
6.812
< 0.1%
6.821
< 0.1%
6.842
< 0.1%
6.851
< 0.1%
6.872
< 0.1%
6.882
< 0.1%
ValueCountFrequency (%)
7.612
 
< 0.1%
7.64
 
< 0.1%
7.593
 
< 0.1%
7.583
 
< 0.1%
7.574
 
< 0.1%
7.569
 
< 0.1%
7.556
 
< 0.1%
7.5420
0.1%
7.5312
0.1%
7.5225
0.1%

PaCO2
Real number (ℝ≥0)

MISSING

Distinct372
Distinct (%)6.4%
Missing14221
Missing (%)71.1%
Infinite0
Infinite (%)0.0%
Mean37.65852224
Minimum12
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:10.202840image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile26
Q132
median36.5
Q341.3
95-th percentile54
Maximum100
Range88
Interquartile range (IQR)9.3

Descriptive statistics

Standard deviation9.419204372
Coefficient of variation (CV)0.2501214549
Kurtosis5.640452899
Mean37.65852224
Median Absolute Deviation (MAD)4.5
Skewness1.638943563
Sum217628.6
Variance88.721411
MonotonicityNot monotonic
2021-11-29T11:25:10.305791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34273
 
1.4%
36266
 
1.3%
38245
 
1.2%
35238
 
1.2%
32235
 
1.2%
37227
 
1.1%
40205
 
1.0%
33197
 
1.0%
39189
 
0.9%
31181
 
0.9%
Other values (362)3523
 
17.6%
(Missing)14221
71.1%
ValueCountFrequency (%)
121
 
< 0.1%
131
 
< 0.1%
157
< 0.1%
15.31
 
< 0.1%
165
< 0.1%
16.21
 
< 0.1%
16.42
 
< 0.1%
16.71
 
< 0.1%
1711
0.1%
189
< 0.1%
ValueCountFrequency (%)
1001
 
< 0.1%
951
 
< 0.1%
93.41
 
< 0.1%
931
 
< 0.1%
911
 
< 0.1%
892
< 0.1%
88.11
 
< 0.1%
881
 
< 0.1%
873
< 0.1%
861
 
< 0.1%

SaO2
Real number (ℝ≥0)

MISSING

Distinct298
Distinct (%)5.8%
Missing14875
Missing (%)74.4%
Infinite0
Infinite (%)0.0%
Mean95.48681951
Minimum23
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:10.414148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile88
Q194.4
median96.7
Q398.3
95-th percentile99.4
Maximum100
Range77
Interquartile range (IQR)3.9

Descriptive statistics

Standard deviation4.919157181
Coefficient of variation (CV)0.05151660937
Kurtosis36.07666916
Mean95.48681951
Median Absolute Deviation (MAD)1.8
Skewness-4.524774396
Sum489369.95
Variance24.19810737
MonotonicityNot monotonic
2021-11-29T11:25:10.522208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98.8120
 
0.6%
99113
 
0.6%
98.6102
 
0.5%
98.798
 
0.5%
98.298
 
0.5%
9795
 
0.5%
99.294
 
0.5%
97.692
 
0.5%
98.492
 
0.5%
97.888
 
0.4%
Other values (288)4133
 
20.7%
(Missing)14875
74.4%
ValueCountFrequency (%)
231
< 0.1%
29.11
< 0.1%
36.61
< 0.1%
45.21
< 0.1%
50.31
< 0.1%
52.51
< 0.1%
54.71
< 0.1%
56.61
< 0.1%
58.21
< 0.1%
58.31
< 0.1%
ValueCountFrequency (%)
1001
 
< 0.1%
99.930
0.1%
99.838
0.2%
99.748
0.2%
99.654
0.3%
99.552
 
< 0.1%
99.564
0.3%
99.452
 
< 0.1%
99.471
0.4%
99.358
0.3%

AST
Real number (ℝ≥0)

MISSING

Distinct542
Distinct (%)6.4%
Missing11536
Missing (%)57.7%
Infinite0
Infinite (%)0.0%
Mean74.53308129
Minimum5
Maximum8567
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:10.638042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile12
Q118
median27
Q348
95-th percentile216.85
Maximum8567
Range8562
Interquartile range (IQR)30

Descriptive statistics

Standard deviation280.7648406
Coefficient of variation (CV)3.766982871
Kurtosis310.0415639
Mean74.53308129
Median Absolute Deviation (MAD)11
Skewness15.09768056
Sum630848
Variance78828.89569
MonotonicityNot monotonic
2021-11-29T11:25:10.742960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17324
 
1.6%
20307
 
1.5%
16306
 
1.5%
19306
 
1.5%
18304
 
1.5%
21293
 
1.5%
15270
 
1.4%
22267
 
1.3%
14259
 
1.3%
24251
 
1.3%
Other values (532)5577
27.9%
(Missing)11536
57.7%
ValueCountFrequency (%)
59
 
< 0.1%
68
 
< 0.1%
79
 
< 0.1%
825
 
0.1%
943
 
0.2%
1080
 
0.4%
11126
0.6%
12175
0.9%
13187
0.9%
14259
1.3%
ValueCountFrequency (%)
85671
< 0.1%
79061
< 0.1%
65601
< 0.1%
56941
< 0.1%
51551
< 0.1%
50121
< 0.1%
48971
< 0.1%
45061
< 0.1%
44331
< 0.1%
43401
< 0.1%

BUN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct167
Distinct (%)0.9%
Missing1591
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean19.24591233
Minimum1
Maximum177
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:10.851975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q110
median15
Q322
95-th percentile51
Maximum177
Range176
Interquartile range (IQR)12

Descriptive statistics

Standard deviation16.02085159
Coefficient of variation (CV)0.8324287939
Kurtosis11.64271136
Mean19.24591233
Median Absolute Deviation (MAD)6
Skewness2.836883334
Sum354298
Variance256.6676855
MonotonicityNot monotonic
2021-11-29T11:25:10.958742image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
121068
 
5.3%
131066
 
5.3%
111040
 
5.2%
10991
 
5.0%
9909
 
4.5%
14892
 
4.5%
8860
 
4.3%
15844
 
4.2%
16755
 
3.8%
17731
 
3.7%
Other values (157)9253
46.3%
(Missing)1591
 
8.0%
ValueCountFrequency (%)
147
 
0.2%
274
 
0.4%
3183
 
0.9%
3.51
 
< 0.1%
4307
1.5%
4.52
 
< 0.1%
5438
2.2%
5.52
 
< 0.1%
6571
2.9%
7719
3.6%
ValueCountFrequency (%)
1771
< 0.1%
1731
< 0.1%
1701
< 0.1%
1611
< 0.1%
1571
< 0.1%
1521
< 0.1%
1511
< 0.1%
1491
< 0.1%
1451
< 0.1%
1421
< 0.1%

Alkalinephos
Real number (ℝ≥0)

MISSING

Distinct422
Distinct (%)5.0%
Missing11530
Missing (%)57.6%
Infinite0
Infinite (%)0.0%
Mean85.2688902
Minimum11
Maximum1650
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:11.072318image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile32.45
Q151
median67
Q394
95-th percentile189
Maximum1650
Range1639
Interquartile range (IQR)43

Descriptive statistics

Standard deviation77.18846698
Coefficient of variation (CV)0.9052359753
Kurtosis80.63166588
Mean85.2688902
Median Absolute Deviation (MAD)19
Skewness6.819773184
Sum722227.5
Variance5958.059435
MonotonicityNot monotonic
2021-11-29T11:25:11.177899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56150
 
0.8%
58146
 
0.7%
52140
 
0.7%
54139
 
0.7%
51139
 
0.7%
53136
 
0.7%
49135
 
0.7%
61134
 
0.7%
57134
 
0.7%
46132
 
0.7%
Other values (412)7085
35.4%
(Missing)11530
57.6%
ValueCountFrequency (%)
112
 
< 0.1%
121
 
< 0.1%
132
 
< 0.1%
142
 
< 0.1%
155
< 0.1%
167
< 0.1%
177
< 0.1%
186
< 0.1%
1910
0.1%
209
< 0.1%
ValueCountFrequency (%)
16502
< 0.1%
12141
< 0.1%
11601
< 0.1%
11291
< 0.1%
10721
< 0.1%
9871
< 0.1%
9721
< 0.1%
9581
< 0.1%
9351
< 0.1%
9281
< 0.1%

Calcium
Real number (ℝ≥0)

MISSING

Distinct327
Distinct (%)1.8%
Missing1550
Missing (%)7.8%
Infinite0
Infinite (%)0.0%
Mean6.739645528
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:11.288189image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.09
Q16.8
median8.1
Q38.6
95-th percentile9.3
Maximum27
Range26
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation3.067395095
Coefficient of variation (CV)0.455127066
Kurtosis0.02832909665
Mean6.739645528
Median Absolute Deviation (MAD)0.7
Skewness-1.024280748
Sum124346.46
Variance9.408912672
MonotonicityNot monotonic
2021-11-29T11:25:11.393495image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.3938
 
4.7%
8.5921
 
4.6%
8.2896
 
4.5%
8.6886
 
4.4%
8.4885
 
4.4%
8.1811
 
4.1%
8.8770
 
3.9%
8.7768
 
3.8%
8725
 
3.6%
8.9666
 
3.3%
Other values (317)10184
50.9%
(Missing)1550
 
7.8%
ValueCountFrequency (%)
152
 
0.3%
1.0155
 
0.3%
1.0274
0.4%
1.0371
0.4%
1.0493
0.5%
1.0578
0.4%
1.06113
0.6%
1.07108
0.5%
1.08155
0.8%
1.09169
0.8%
ValueCountFrequency (%)
271
 
< 0.1%
25.21
 
< 0.1%
23.71
 
< 0.1%
191
 
< 0.1%
18.82
 
< 0.1%
18.63
< 0.1%
18.24
< 0.1%
182
 
< 0.1%
17.85
< 0.1%
17.61
 
< 0.1%

Chloride
Real number (ℝ≥0)

MISSING

Distinct56
Distinct (%)3.5%
Missing18383
Missing (%)91.9%
Infinite0
Infinite (%)0.0%
Mean104.9931973
Minimum74
Maximum124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:11.496244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum74
5-th percentile96
Q1103
median105
Q3108
95-th percentile112
Maximum124
Range50
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.120541116
Coefficient of variation (CV)0.04877021796
Kurtosis3.206393389
Mean104.9931973
Median Absolute Deviation (MAD)3
Skewness-0.8108677937
Sum169774
Variance26.21994132
MonotonicityNot monotonic
2021-11-29T11:25:11.603116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105161
 
0.8%
107160
 
0.8%
106155
 
0.8%
108148
 
0.7%
104127
 
0.6%
103118
 
0.6%
10998
 
0.5%
10294
 
0.5%
11079
 
0.4%
10156
 
0.3%
Other values (46)421
 
2.1%
(Missing)18383
91.9%
ValueCountFrequency (%)
741
 
< 0.1%
781
 
< 0.1%
801
 
< 0.1%
821
 
< 0.1%
832
 
< 0.1%
851
 
< 0.1%
863
< 0.1%
885
< 0.1%
893
< 0.1%
903
< 0.1%
ValueCountFrequency (%)
1243
 
< 0.1%
1231
 
< 0.1%
1221
 
< 0.1%
1201
 
< 0.1%
1193
 
< 0.1%
1184
 
< 0.1%
1177
< 0.1%
1161
 
< 0.1%
1157
< 0.1%
11415
0.1%

Creatinine
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1018
Distinct (%)5.5%
Missing1588
Missing (%)7.9%
Infinite0
Infinite (%)0.0%
Mean1.445464371
Minimum0.2
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:11.790714image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.49
Q10.69
median0.89
Q31.26
95-th percentile5.02
Maximum25
Range24.8
Interquartile range (IQR)0.57

Descriptive statistics

Standard deviation1.902371228
Coefficient of variation (CV)1.316096935
Kurtosis28.92521167
Mean1.445464371
Median Absolute Deviation (MAD)0.24
Skewness4.707814211
Sum26613.89
Variance3.619016291
MonotonicityNot monotonic
2021-11-29T11:25:11.898102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8281
 
1.4%
0.81279
 
1.4%
0.79275
 
1.4%
0.82263
 
1.3%
0.77259
 
1.3%
0.69258
 
1.3%
0.73254
 
1.3%
0.68244
 
1.2%
0.78242
 
1.2%
0.72241
 
1.2%
Other values (1008)15816
79.1%
(Missing)1588
 
7.9%
ValueCountFrequency (%)
0.24
 
< 0.1%
0.211
 
< 0.1%
0.222
 
< 0.1%
0.231
 
< 0.1%
0.241
 
< 0.1%
0.251
 
< 0.1%
0.272
 
< 0.1%
0.285
 
< 0.1%
0.293
 
< 0.1%
0.376
0.4%
ValueCountFrequency (%)
251
< 0.1%
23.831
< 0.1%
23.711
< 0.1%
23.651
< 0.1%
22.961
< 0.1%
22.011
< 0.1%
21.971
< 0.1%
21.461
< 0.1%
21.311
< 0.1%
21.181
< 0.1%

Bilirubin_direct
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct158
Distinct (%)10.7%
Missing18529
Missing (%)92.6%
Infinite0
Infinite (%)0.0%
Mean0.6810605031
Minimum0.01
Maximum20.57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:12.004743image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.1
Q10.1
median0.2
Q30.4
95-th percentile2.35
Maximum20.57
Range20.56
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation1.918581063
Coefficient of variation (CV)2.817049373
Kurtosis58.88859506
Mean0.6810605031
Median Absolute Deviation (MAD)0.1
Skewness7.117946386
Sum1001.84
Variance3.680953296
MonotonicityNot monotonic
2021-11-29T11:25:12.105247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1449
 
2.2%
0.2275
 
1.4%
0.3125
 
0.6%
0.487
 
0.4%
0.540
 
0.2%
0.633
 
0.2%
123
 
0.1%
0.721
 
0.1%
1.114
 
0.1%
1.213
 
0.1%
Other values (148)391
 
2.0%
(Missing)18529
92.6%
ValueCountFrequency (%)
0.017
 
< 0.1%
0.024
 
< 0.1%
0.035
 
< 0.1%
0.044
 
< 0.1%
0.056
 
< 0.1%
0.066
 
< 0.1%
0.077
 
< 0.1%
0.084
 
< 0.1%
0.0911
 
0.1%
0.1449
2.2%
ValueCountFrequency (%)
20.571
< 0.1%
202
< 0.1%
19.541
< 0.1%
19.081
< 0.1%
19.051
< 0.1%
18.181
< 0.1%
17.41
< 0.1%
16.61
< 0.1%
15.61
< 0.1%
151
< 0.1%

Glucose
Real number (ℝ≥0)

MISSING

Distinct394
Distinct (%)2.1%
Missing1173
Missing (%)5.9%
Infinite0
Infinite (%)0.0%
Mean101.9483587
Minimum13
Maximum409
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:12.211030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile66
Q186
median98
Q3114
95-th percentile151
Maximum409
Range396
Interquartile range (IQR)28

Descriptive statistics

Standard deviation27.98982318
Coefficient of variation (CV)0.2745490318
Kurtosis8.007447523
Mean101.9483587
Median Absolute Deviation (MAD)14
Skewness1.705717808
Sum1919381.75
Variance783.4302018
MonotonicityNot monotonic
2021-11-29T11:25:12.321104image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96405
 
2.0%
95401
 
2.0%
97397
 
2.0%
93391
 
2.0%
90387
 
1.9%
99382
 
1.9%
94380
 
1.9%
91379
 
1.9%
92371
 
1.9%
101371
 
1.9%
Other values (384)14963
74.8%
(Missing)1173
 
5.9%
ValueCountFrequency (%)
131
 
< 0.1%
151
 
< 0.1%
161
 
< 0.1%
211
 
< 0.1%
23.51
 
< 0.1%
261
 
< 0.1%
282
 
< 0.1%
29.51
 
< 0.1%
3023
0.1%
30.51
 
< 0.1%
ValueCountFrequency (%)
4091
< 0.1%
3891
< 0.1%
369.51
< 0.1%
3671
< 0.1%
3541
< 0.1%
349.51
< 0.1%
3231
< 0.1%
3221
< 0.1%
3001
< 0.1%
2971
< 0.1%

Lactate
Real number (ℝ≥0)

MISSING

Distinct493
Distinct (%)10.4%
Missing15240
Missing (%)76.2%
Infinite0
Infinite (%)0.0%
Mean1.765405462
Minimum0.4
Maximum19.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:12.436146image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.7495
Q11.07
median1.41
Q31.98
95-th percentile3.7605
Maximum19.12
Range18.72
Interquartile range (IQR)0.91

Descriptive statistics

Standard deviation1.383693755
Coefficient of variation (CV)0.7837824143
Kurtosis38.8292031
Mean1.765405462
Median Absolute Deviation (MAD)0.41
Skewness5.058561021
Sum8403.33
Variance1.914608409
MonotonicityNot monotonic
2021-11-29T11:25:12.544245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.269
 
0.3%
0.863
 
0.3%
161
 
0.3%
1.161
 
0.3%
0.953
 
0.3%
1.2349
 
0.2%
1.348
 
0.2%
0.747
 
0.2%
1.3144
 
0.2%
1.443
 
0.2%
Other values (483)4222
 
21.1%
(Missing)15240
76.2%
ValueCountFrequency (%)
0.41
 
< 0.1%
0.461
 
< 0.1%
0.511
0.1%
0.533
 
< 0.1%
0.542
 
< 0.1%
0.552
 
< 0.1%
0.564
 
< 0.1%
0.574
 
< 0.1%
0.581
 
< 0.1%
0.592
 
< 0.1%
ValueCountFrequency (%)
19.121
< 0.1%
17.752
< 0.1%
17.421
< 0.1%
16.151
< 0.1%
15.941
< 0.1%
151
< 0.1%
14.441
< 0.1%
13.671
< 0.1%
13.061
< 0.1%
12.981
< 0.1%

Magnesium
Real number (ℝ≥0)

MISSING

Distinct58
Distinct (%)0.4%
Missing3543
Missing (%)17.7%
Infinite0
Infinite (%)0.0%
Mean1.904144133
Minimum0.5
Maximum6.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:12.657165image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1.4
Q11.7
median1.9
Q32.1
95-th percentile2.4
Maximum6.2
Range5.7
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.3193901207
Coefficient of variation (CV)0.1677342146
Kurtosis9.487936571
Mean1.904144133
Median Absolute Deviation (MAD)0.2
Skewness1.209795446
Sum31336.5
Variance0.1020100492
MonotonicityNot monotonic
2021-11-29T11:25:12.755407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.92741
13.7%
1.82459
12.3%
22113
10.6%
1.71793
9.0%
2.11619
8.1%
1.61220
 
6.1%
2.21074
 
5.4%
1.5733
 
3.7%
2.3681
 
3.4%
1.4435
 
2.2%
Other values (48)1589
7.9%
(Missing)3543
17.7%
ValueCountFrequency (%)
0.52
 
< 0.1%
0.61
 
< 0.1%
0.71
 
< 0.1%
0.86
 
< 0.1%
0.915
 
0.1%
137
 
0.2%
1.166
 
0.3%
1.2127
0.6%
1.3250
1.2%
1.351
 
< 0.1%
ValueCountFrequency (%)
6.21
< 0.1%
61
< 0.1%
5.41
< 0.1%
5.11
< 0.1%
51
< 0.1%
4.92
< 0.1%
4.81
< 0.1%
4.51
< 0.1%
4.31
< 0.1%
4.21
< 0.1%

Phosphate
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct125
Distinct (%)1.1%
Missing8365
Missing (%)41.8%
Infinite0
Infinite (%)0.0%
Mean3.240825097
Minimum0.6
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:12.858147image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile1.5
Q12.4
median3.1
Q33.8
95-th percentile5.5
Maximum12
Range11.4
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.282144642
Coefficient of variation (CV)0.3956229059
Kurtosis4.757043014
Mean3.240825097
Median Absolute Deviation (MAD)0.7
Skewness1.444106148
Sum37707
Variance1.643894884
MonotonicityNot monotonic
2021-11-29T11:25:12.967990image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3459
 
2.3%
2.9454
 
2.3%
2.8443
 
2.2%
3.2436
 
2.2%
3.1433
 
2.2%
3.4429
 
2.1%
3.3427
 
2.1%
3.5417
 
2.1%
2.5414
 
2.1%
2.4413
 
2.1%
Other values (115)7310
36.5%
(Missing)8365
41.8%
ValueCountFrequency (%)
0.625
 
0.1%
0.716
 
0.1%
0.832
 
0.2%
0.851
 
< 0.1%
0.919
 
0.1%
180
0.4%
1.143
0.2%
1.276
0.4%
1.381
0.4%
1.496
0.5%
ValueCountFrequency (%)
126
< 0.1%
11.81
 
< 0.1%
11.71
 
< 0.1%
11.61
 
< 0.1%
11.51
 
< 0.1%
11.42
 
< 0.1%
11.31
 
< 0.1%
11.21
 
< 0.1%
111
 
< 0.1%
10.91
 
< 0.1%

Potassium
Real number (ℝ≥0)

MISSING

Distinct242
Distinct (%)1.3%
Missing1434
Missing (%)7.2%
Infinite0
Infinite (%)0.0%
Mean3.804321879
Minimum1.3
Maximum9.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:13.077646image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile3
Q13.5
median3.8
Q34.1
95-th percentile4.6
Maximum9.8
Range8.5
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.5139251116
Coefficient of variation (CV)0.1350898079
Kurtosis6.429641893
Mean3.804321879
Median Absolute Deviation (MAD)0.3
Skewness0.8362609904
Sum70631.04
Variance0.2641190203
MonotonicityNot monotonic
2021-11-29T11:25:13.179932image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.81666
 
8.3%
3.71662
 
8.3%
3.91571
 
7.9%
3.61537
 
7.7%
41320
 
6.6%
3.51269
 
6.3%
4.11140
 
5.7%
3.41070
 
5.3%
4.2872
 
4.4%
3.3858
 
4.3%
Other values (232)5601
28.0%
(Missing)1434
 
7.2%
ValueCountFrequency (%)
1.33
 
< 0.1%
1.42
 
< 0.1%
1.51
 
< 0.1%
1.72
 
< 0.1%
1.81
 
< 0.1%
1.95
 
< 0.1%
27
< 0.1%
2.16
 
< 0.1%
2.215
0.1%
2.317
0.1%
ValueCountFrequency (%)
9.81
 
< 0.1%
9.42
< 0.1%
9.22
< 0.1%
8.21
 
< 0.1%
7.81
 
< 0.1%
7.63
< 0.1%
7.491
 
< 0.1%
7.42
< 0.1%
7.21
 
< 0.1%
7.11
 
< 0.1%

Bilirubin_total
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct170
Distinct (%)2.0%
Missing11522
Missing (%)57.6%
Infinite0
Infinite (%)0.0%
Mean1.146815287
Minimum0.1
Maximum49.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:13.366303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.3
Q10.5
median0.7
Q31.1
95-th percentile2.9
Maximum49.2
Range49.1
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation2.09573636
Coefficient of variation (CV)1.827440203
Kurtosis138.5951719
Mean1.146815287
Median Absolute Deviation (MAD)0.3
Skewness10.10737183
Sum9722.7
Variance4.39211089
MonotonicityNot monotonic
2021-11-29T11:25:13.476110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6946
 
4.7%
0.5943
 
4.7%
0.4869
 
4.3%
0.7836
 
4.2%
0.8701
 
3.5%
0.9539
 
2.7%
0.3516
 
2.6%
1466
 
2.3%
1.1347
 
1.7%
1.2260
 
1.3%
Other values (160)2055
 
10.3%
(Missing)11522
57.6%
ValueCountFrequency (%)
0.154
 
0.3%
0.151
 
< 0.1%
0.2219
 
1.1%
0.252
 
< 0.1%
0.3516
2.6%
0.352
 
< 0.1%
0.4869
4.3%
0.452
 
< 0.1%
0.5943
4.7%
0.552
 
< 0.1%
ValueCountFrequency (%)
49.21
< 0.1%
40.32
< 0.1%
36.21
< 0.1%
34.61
< 0.1%
34.31
< 0.1%
29.41
< 0.1%
29.31
< 0.1%
28.21
< 0.1%
27.21
< 0.1%
271
< 0.1%

TroponinI
Real number (ℝ≥0)

MISSING

Distinct972
Distinct (%)14.8%
Missing13436
Missing (%)67.2%
Infinite0
Infinite (%)0.0%
Mean3.581375686
Minimum0.01
Maximum349.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:13.584328image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.01
Q10.02
median0.06
Q30.56
95-th percentile21.1825
Maximum349.05
Range349.04
Interquartile range (IQR)0.54

Descriptive statistics

Standard deviation14.32076588
Coefficient of variation (CV)3.998677362
Kurtosis117.394797
Mean3.581375686
Median Absolute Deviation (MAD)0.05
Skewness8.817558162
Sum23508.15
Variance205.0843353
MonotonicityNot monotonic
2021-11-29T11:25:13.688923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.011342
 
6.7%
0.03894
 
4.5%
0.04334
 
1.7%
0.02332
 
1.7%
0.05215
 
1.1%
0.06188
 
0.9%
0.07160
 
0.8%
0.08126
 
0.6%
0.09111
 
0.6%
0.192
 
0.5%
Other values (962)2770
 
13.9%
(Missing)13436
67.2%
ValueCountFrequency (%)
0.011342
6.7%
0.02332
 
1.7%
0.03894
4.5%
0.04334
 
1.7%
0.05215
 
1.1%
0.06188
 
0.9%
0.07160
 
0.8%
0.08126
 
0.6%
0.09111
 
0.6%
0.192
 
0.5%
ValueCountFrequency (%)
349.051
 
< 0.1%
219.621
 
< 0.1%
2005
< 0.1%
180.081
 
< 0.1%
1671
 
< 0.1%
164.531
 
< 0.1%
155.651
 
< 0.1%
153.341
 
< 0.1%
151.831
 
< 0.1%
149.341
 
< 0.1%

Hct
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct436
Distinct (%)2.4%
Missing1953
Missing (%)9.8%
Infinite0
Infinite (%)0.0%
Mean31.26301213
Minimum9.1
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:13.799039image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum9.1
5-th percentile20.4
Q126.2
median31.3
Q336.2
95-th percentile42.2
Maximum65
Range55.9
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.804708332
Coefficient of variation (CV)0.2176600355
Kurtosis-0.3645388506
Mean31.26301213
Median Absolute Deviation (MAD)5
Skewness0.08356011901
Sum564203.58
Variance46.30405548
MonotonicityNot monotonic
2021-11-29T11:25:13.899096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33113
 
0.6%
34111
 
0.6%
32.4110
 
0.5%
32.9109
 
0.5%
37.3105
 
0.5%
27104
 
0.5%
34.3104
 
0.5%
30.1103
 
0.5%
29103
 
0.5%
24103
 
0.5%
Other values (426)16982
84.9%
(Missing)1953
 
9.8%
ValueCountFrequency (%)
9.11
< 0.1%
9.31
< 0.1%
9.61
< 0.1%
10.71
< 0.1%
10.81
< 0.1%
11.51
< 0.1%
11.61
< 0.1%
12.41
< 0.1%
12.62
< 0.1%
12.72
< 0.1%
ValueCountFrequency (%)
651
< 0.1%
63.41
< 0.1%
63.21
< 0.1%
58.81
< 0.1%
57.71
< 0.1%
56.11
< 0.1%
55.61
< 0.1%
55.31
< 0.1%
55.21
< 0.1%
54.91
< 0.1%

Hgb
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct216
Distinct (%)1.2%
Missing1941
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean10.23865829
Minimum2.3
Maximum26.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:14.002564image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.3
5-th percentile6.7
Q18.5
median10.2
Q311.9
95-th percentile14.1
Maximum26.6
Range24.3
Interquartile range (IQR)3.4

Descriptive statistics

Standard deviation2.330054737
Coefficient of variation (CV)0.2275742262
Kurtosis0.09243264671
Mean10.23865829
Median Absolute Deviation (MAD)1.7
Skewness0.2868288395
Sum184899.93
Variance5.429155078
MonotonicityNot monotonic
2021-11-29T11:25:14.109106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.9298
 
1.5%
10.5297
 
1.5%
9.1285
 
1.4%
10.9284
 
1.4%
9283
 
1.4%
10.7280
 
1.4%
10.3279
 
1.4%
9.2278
 
1.4%
11.3277
 
1.4%
9.4275
 
1.4%
Other values (206)15223
76.1%
(Missing)1941
 
9.7%
ValueCountFrequency (%)
2.31
 
< 0.1%
2.61
 
< 0.1%
2.81
 
< 0.1%
2.91
 
< 0.1%
31
 
< 0.1%
3.62
< 0.1%
3.72
< 0.1%
3.83
< 0.1%
3.91
 
< 0.1%
43
< 0.1%
ValueCountFrequency (%)
26.61
< 0.1%
24.82
< 0.1%
23.81
< 0.1%
23.41
< 0.1%
21.61
< 0.1%
21.21
< 0.1%
211
< 0.1%
20.61
< 0.1%
20.31
< 0.1%
19.61
< 0.1%

PTT
Real number (ℝ≥0)

MISSING

Distinct566
Distinct (%)12.9%
Missing15602
Missing (%)78.0%
Infinite0
Infinite (%)0.0%
Mean35.85675307
Minimum20
Maximum250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:14.213155image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile23.3
Q127.4
median30.5
Q335.3
95-th percentile62.5
Maximum250
Range230
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation22.81357629
Coefficient of variation (CV)0.6362421116
Kurtosis43.9425364
Mean35.85675307
Median Absolute Deviation (MAD)3.6
Skewness5.941973984
Sum157698
Variance520.459263
MonotonicityNot monotonic
2021-11-29T11:25:14.321616image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.346
 
0.2%
2845
 
0.2%
29.645
 
0.2%
30.745
 
0.2%
3044
 
0.2%
30.843
 
0.2%
30.443
 
0.2%
27.440
 
0.2%
31.340
 
0.2%
31.739
 
0.2%
Other values (556)3968
 
19.8%
(Missing)15602
78.0%
ValueCountFrequency (%)
2038
0.2%
20.15
 
< 0.1%
20.32
 
< 0.1%
20.43
 
< 0.1%
20.53
 
< 0.1%
20.64
 
< 0.1%
20.71
 
< 0.1%
20.83
 
< 0.1%
20.97
 
< 0.1%
214
 
< 0.1%
ValueCountFrequency (%)
2503
 
< 0.1%
249.94
 
< 0.1%
24912
0.1%
237.51
 
< 0.1%
216.51
 
< 0.1%
212.31
 
< 0.1%
204.91
 
< 0.1%
200.81
 
< 0.1%
196.81
 
< 0.1%
195.51
 
< 0.1%

WBC
Real number (ℝ≥0)

MISSING

Distinct405
Distinct (%)2.2%
Missing2000
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean9.320386667
Minimum0.1
Maximum296.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:14.429912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile3.9
Q16.3
median8.5
Q311.2
95-th percentile16.9
Maximum296.1
Range296
Interquartile range (IQR)4.9

Descriptive statistics

Standard deviation5.69375804
Coefficient of variation (CV)0.6108929
Kurtosis491.5408061
Mean9.320386667
Median Absolute Deviation (MAD)2.4
Skewness13.90233237
Sum167766.96
Variance32.41888062
MonotonicityNot monotonic
2021-11-29T11:25:14.531154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.2250
 
1.2%
7239
 
1.2%
7.4238
 
1.2%
8.6232
 
1.2%
7.6230
 
1.1%
7.8229
 
1.1%
8224
 
1.1%
6222
 
1.1%
6.4222
 
1.1%
8.2219
 
1.1%
Other values (395)15695
78.5%
(Missing)2000
 
10.0%
ValueCountFrequency (%)
0.112
0.1%
0.24
 
< 0.1%
0.33
 
< 0.1%
0.45
< 0.1%
0.52
 
< 0.1%
0.64
 
< 0.1%
0.75
< 0.1%
0.82
 
< 0.1%
0.93
 
< 0.1%
15
< 0.1%
ValueCountFrequency (%)
296.11
< 0.1%
152.91
< 0.1%
150.61
< 0.1%
144.91
< 0.1%
142.21
< 0.1%
140.41
< 0.1%
137.71
< 0.1%
119.51
< 0.1%
1181
< 0.1%
104.11
< 0.1%

Fibrinogen
Real number (ℝ≥0)

MISSING

Distinct518
Distinct (%)26.6%
Missing18052
Missing (%)90.3%
Infinite0
Infinite (%)0.0%
Mean270.0433778
Minimum35
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:14.641638image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile101.35
Q1176
median233
Q3324
95-th percentile573.15
Maximum1000
Range965
Interquartile range (IQR)148

Descriptive statistics

Standard deviation146.7863663
Coefficient of variation (CV)0.54356588
Kurtosis3.861013099
Mean270.0433778
Median Absolute Deviation (MAD)71
Skewness1.657538313
Sum526044.5
Variance21546.23733
MonotonicityNot monotonic
2021-11-29T11:25:14.751693image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21722
 
0.1%
20018
 
0.1%
23015
 
0.1%
21315
 
0.1%
24014
 
0.1%
15114
 
0.1%
21914
 
0.1%
20214
 
0.1%
21413
 
0.1%
20813
 
0.1%
Other values (508)1796
 
9.0%
(Missing)18052
90.3%
ValueCountFrequency (%)
358
< 0.1%
411
 
< 0.1%
422
 
< 0.1%
463
 
< 0.1%
482
 
< 0.1%
501
 
< 0.1%
512
 
< 0.1%
524
< 0.1%
531
 
< 0.1%
571
 
< 0.1%
ValueCountFrequency (%)
10006
< 0.1%
9541
 
< 0.1%
9451
 
< 0.1%
9191
 
< 0.1%
9121
 
< 0.1%
8881
 
< 0.1%
8821
 
< 0.1%
8781
 
< 0.1%
8671
 
< 0.1%
8361
 
< 0.1%

Platelets
Real number (ℝ≥0)

MISSING

Distinct624
Distinct (%)3.5%
Missing1992
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean190.2616892
Minimum1
Maximum2322
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:14.941279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile65
Q1129
median180
Q3238
95-th percentile348.65
Maximum2322
Range2321
Interquartile range (IQR)109

Descriptive statistics

Standard deviation92.41697197
Coefficient of variation (CV)0.4857361056
Kurtosis19.13541482
Mean190.2616892
Median Absolute Deviation (MAD)54
Skewness1.846470537
Sum3426232.5
Variance8540.896709
MonotonicityNot monotonic
2021-11-29T11:25:15.048911image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180109
 
0.5%
141109
 
0.5%
183107
 
0.5%
162105
 
0.5%
166105
 
0.5%
167102
 
0.5%
168101
 
0.5%
175101
 
0.5%
159101
 
0.5%
18298
 
0.5%
Other values (614)16970
84.9%
(Missing)1992
 
10.0%
ValueCountFrequency (%)
11
 
< 0.1%
24
< 0.1%
32
 
< 0.1%
47
< 0.1%
52
 
< 0.1%
61
 
< 0.1%
72
 
< 0.1%
83
< 0.1%
92
 
< 0.1%
101
 
< 0.1%
ValueCountFrequency (%)
23221
< 0.1%
9201
< 0.1%
8381
< 0.1%
8221
< 0.1%
8111
< 0.1%
8061
< 0.1%
8031
< 0.1%
7921
< 0.1%
7821
< 0.1%
7731
< 0.1%

Age
Real number (ℝ≥0)

Distinct77
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.6488
Minimum14
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:15.159310image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile30
Q150
median62
Q372
95-th percentile85
Maximum100
Range86
Interquartile range (IQR)22

Descriptive statistics

Standard deviation16.67181022
Coefficient of variation (CV)0.2748910155
Kurtosis-0.1562598942
Mean60.6488
Median Absolute Deviation (MAD)11
Skewness-0.2649819802
Sum1212976
Variance277.949256
MonotonicityNot monotonic
2021-11-29T11:25:15.263870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67572
 
2.9%
68539
 
2.7%
66512
 
2.6%
65510
 
2.5%
61498
 
2.5%
69495
 
2.5%
71481
 
2.4%
62477
 
2.4%
63470
 
2.4%
70467
 
2.3%
Other values (67)14979
74.9%
ValueCountFrequency (%)
142
 
< 0.1%
152
 
< 0.1%
165
 
< 0.1%
1713
 
0.1%
1832
 
0.2%
1954
0.3%
2065
0.3%
2199
0.5%
2259
0.3%
2377
0.4%
ValueCountFrequency (%)
100392
2.0%
89111
 
0.6%
88138
 
0.7%
87145
 
0.7%
86187
0.9%
85187
0.9%
84206
1.0%
83247
1.2%
82242
1.2%
81224
1.1%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
1
10732 
0
9268 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
110732
53.7%
09268
46.3%

Length

2021-11-29T11:25:15.361053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:25:15.418566image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
110732
53.7%
09268
46.3%

Most occurring characters

ValueCountFrequency (%)
110732
53.7%
09268
46.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
110732
53.7%
09268
46.3%

Most occurring scripts

ValueCountFrequency (%)
Common20000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
110732
53.7%
09268
46.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
110732
53.7%
09268
46.3%

Unit1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing6095
Missing (%)30.5%
Memory size156.4 KiB
0.0
6982 
1.0
6923 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41715
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.06982
34.9%
1.06923
34.6%
(Missing)6095
30.5%

Length

2021-11-29T11:25:15.477195image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:25:15.533422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.06982
50.2%
1.06923
49.8%

Most occurring characters

ValueCountFrequency (%)
020887
50.1%
.13905
33.3%
16923
 
16.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27810
66.7%
Other Punctuation13905
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
020887
75.1%
16923
 
24.9%
Other Punctuation
ValueCountFrequency (%)
.13905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41715
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
020887
50.1%
.13905
33.3%
16923
 
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII41715
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
020887
50.1%
.13905
33.3%
16923
 
16.6%

Unit2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing6095
Missing (%)30.5%
Memory size156.4 KiB
1.0
6982 
0.0
6923 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41715
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.06982
34.9%
0.06923
34.6%
(Missing)6095
30.5%

Length

2021-11-29T11:25:15.592733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:25:15.649068image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.06982
50.2%
0.06923
49.8%

Most occurring characters

ValueCountFrequency (%)
020828
49.9%
.13905
33.3%
16982
 
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27810
66.7%
Other Punctuation13905
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
020828
74.9%
16982
 
25.1%
Other Punctuation
ValueCountFrequency (%)
.13905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41715
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
020828
49.9%
.13905
33.3%
16982
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII41715
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
020828
49.9%
.13905
33.3%
16982
 
16.7%

HospAdmTime
Real number (ℝ)

ZEROS

Distinct7975
Distinct (%)39.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-55.072586
Minimum-5366.86
Maximum0
Zeros1145
Zeros (%)5.7%
Negative18855
Negative (%)94.3%
Memory size156.4 KiB
2021-11-29T11:25:15.716761image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-5366.86
5-th percentile-244.829
Q1-52.3525
median-8.57
Q3-3.38
95-th percentile0
Maximum0
Range5366.86
Interquartile range (IQR)48.9725

Descriptive statistics

Standard deviation135.5956936
Coefficient of variation (CV)-2.462126867
Kurtosis241.4449376
Mean-55.072586
Median Absolute Deviation (MAD)8.52
Skewness-10.8324003
Sum-1101451.72
Variance18386.19213
MonotonicityNot monotonic
2021-11-29T11:25:15.830993image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01145
 
5.7%
-0.02250
 
1.2%
-0.03197
 
1.0%
-0.01179
 
0.9%
-0.04136
 
0.7%
-0.05122
 
0.6%
-0.06105
 
0.5%
-0.0794
 
0.5%
-0.0976
 
0.4%
-0.0859
 
0.3%
Other values (7965)17637
88.2%
ValueCountFrequency (%)
-5366.861
< 0.1%
-3397.641
< 0.1%
-3342.341
< 0.1%
-3189.391
< 0.1%
-3112.121
< 0.1%
-2929.371
< 0.1%
-2842.111
< 0.1%
-2667.341
< 0.1%
-2384.781
< 0.1%
-2382.341
< 0.1%
ValueCountFrequency (%)
01145
5.7%
-0.01179
 
0.9%
-0.02250
 
1.2%
-0.03197
 
1.0%
-0.04136
 
0.7%
-0.05122
 
0.6%
-0.06105
 
0.5%
-0.0794
 
0.5%
-0.0859
 
0.3%
-0.0976
 
0.4%

ICULOS
Real number (ℝ≥0)

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.13345
Minimum1
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:15.926826image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum26
Range25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6983304954
Coefficient of variation (CV)0.6161105434
Kurtosis213.6472826
Mean1.13345
Median Absolute Deviation (MAD)0
Skewness10.72522731
Sum22669
Variance0.4876654808
MonotonicityNot monotonic
2021-11-29T11:25:16.010877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
118704
93.5%
2741
 
3.7%
3219
 
1.1%
4146
 
0.7%
577
 
0.4%
654
 
0.3%
725
 
0.1%
814
 
0.1%
97
 
< 0.1%
133
 
< 0.1%
Other values (7)10
 
0.1%
ValueCountFrequency (%)
118704
93.5%
2741
 
3.7%
3219
 
1.1%
4146
 
0.7%
577
 
0.4%
654
 
0.3%
725
 
0.1%
814
 
0.1%
97
 
< 0.1%
103
 
< 0.1%
ValueCountFrequency (%)
261
 
< 0.1%
251
 
< 0.1%
151
 
< 0.1%
141
 
< 0.1%
133
 
< 0.1%
121
 
< 0.1%
112
 
< 0.1%
103
 
< 0.1%
97
< 0.1%
814
0.1%

SepsisLabel
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
0
19777 
1
 
223

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
019777
98.9%
1223
 
1.1%

Length

2021-11-29T11:25:16.101682image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:25:16.159272image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
019777
98.9%
1223
 
1.1%

Most occurring characters

ValueCountFrequency (%)
019777
98.9%
1223
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
019777
98.9%
1223
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common20000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
019777
98.9%
1223
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
019777
98.9%
1223
 
1.1%

Sepsis
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
0
18858 
1
 
1142

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
018858
94.3%
11142
 
5.7%

Length

2021-11-29T11:25:16.218700image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:25:16.276273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
018858
94.3%
11142
 
5.7%

Most occurring characters

ValueCountFrequency (%)
018858
94.3%
11142
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
018858
94.3%
11142
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common20000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
018858
94.3%
11142
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
018858
94.3%
11142
 
5.7%

Hours
Real number (ℝ≥0)

Distinct230
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.09975
Minimum8
Maximum336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:25:16.343320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile14
Q123
median38
Q347
95-th percentile58
Maximum336
Range328
Interquartile range (IQR)24

Descriptive statistics

Standard deviation23.27525267
Coefficient of variation (CV)0.6109030287
Kurtosis44.03440685
Mean38.09975
Median Absolute Deviation (MAD)12
Skewness4.965291886
Sum761995
Variance541.7373868
MonotonicityNot monotonic
2021-11-29T11:25:16.532244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39697
 
3.5%
38667
 
3.3%
36664
 
3.3%
40651
 
3.3%
41639
 
3.2%
43610
 
3.0%
42596
 
3.0%
37591
 
3.0%
21564
 
2.8%
44563
 
2.8%
Other values (220)13758
68.8%
ValueCountFrequency (%)
8204
1.0%
9114
 
0.6%
10126
 
0.6%
11120
 
0.6%
12164
0.8%
13205
1.0%
14218
1.1%
15305
1.5%
16327
1.6%
17373
1.9%
ValueCountFrequency (%)
3365
< 0.1%
3352
 
< 0.1%
3341
 
< 0.1%
3331
 
< 0.1%
3271
 
< 0.1%
3261
 
< 0.1%
3201
 
< 0.1%
3181
 
< 0.1%
3122
 
< 0.1%
3102
 
< 0.1%

Interactions

2021-11-29T11:25:03.889426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:00.102057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:00.213757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:00.313239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:00.415327image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:00.508028image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:00.609580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:00.704574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:00.797078image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:00.893140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:00.983657image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:01.076367image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:01.161959image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:01.335730image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:01.427911image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:01.520786image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:01.620377image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:01.714040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:01.811696image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:01.911128image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:02.003501image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:02.097177image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:02.190069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:02.284035image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:02.384353image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:02.488032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:02.581679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:02.677710image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:02.769027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:02.863613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:02.963446image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:03.057210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:03.147016image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:03.239273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:03.333735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:03.430633image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:03.605799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:03.697826image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:03.795440image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-11-29T11:25:16.688309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-29T11:25:17.058139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-29T11:25:17.426931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-29T11:25:17.734715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-11-29T11:25:04.148218image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-29T11:25:05.318080image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-11-29T11:25:06.074983image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-11-29T11:25:06.947423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

PatientIDHRO2SatTempSBPMAPDBPRespEtCO2BaseExcessHCO3FiO2pHPaCO2SaO2ASTBUNAlkalinephosCalciumChlorideCreatinineBilirubin_directGlucoseLactateMagnesiumPhosphatePotassiumBilirubin_totalTroponinIHctHgbPTTWBCFibrinogenPlateletsAgeGenderUnit1Unit2HospAdmTimeICULOSSepsisLabelSepsisHours
010000191.092.036.5104.075.055.016.0NaNNaNNaNNaNNaNNaNNaNNaN30.0NaN7.80NaN1.50NaN112.0NaN2.0NaN3.70NaNNaN35.311.3NaN10.8NaN170.07311.00.0-214.6410024
110000256.094.035.286.056.041.016.035.0NaNNaNNaNNaNNaNNaNNaN17.0NaN8.20NaN0.84NaN68.0NaN2.21.83.80NaN3.7031.411.1NaN13.2NaN85.08310.01.0-123.1710025
210000394.086.036.496.073.056.017.0NaNNaNNaNNaNNaNNaNNaN5012.019.0327.07.70NaN1.183.371.0NaN1.84.24.704.20.3532.310.8NaN12.6NaN258.0481NaNNaN-2.8310043
310000464.096.036.7101.064.052.010.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN6711.00.0-1.9310059
410000554.076.536.088.058.037.018.0NaNNaNNaN0.47.5223.592.540.044.0231.01.09NaN1.17NaN89.02.711.84.53.740.60.0220.36.624.110.6NaN341.05011.00.0-3.2710052
510000656.093.036.0122.075.054.013.0NaNNaN22.6NaNNaN37.098.538.013.051.07.80NaN0.64NaN165.0NaN1.85.13.801.1NaN43.314.624.27.7NaN137.0501NaNNaN0.0060047
610000776.094.036.8115.084.062.010.0NaNNaNNaNNaNNaNNaNNaN34.014.0112.08.40NaN0.400.1125.0NaN1.74.03.800.5NaN25.07.926.67.0NaN403.0421NaNNaN-1145.9710037
710000871.059.035.891.063.540.013.021.0NaNNaN0.47.1930.0NaNNaN38.0NaN1.13104.02.17NaN82.01.312.43.25.10NaNNaN21.27.0NaN7.6NaN93.06510.01.0-211.6410050
810000956.094.036.1103.054.049.014.0NaNNaNNaNNaNNaNNaNNaNNaN12.0NaN9.30NaN0.71NaN103.0NaN1.9NaN3.90NaNNaN36.211.8NaN5.7NaN201.0820NaNNaN-128.3910030
910001084.094.036.8123.081.067.08.0NaNNaNNaNNaNNaNNaNNaNNaN34.0NaN9.00NaN1.77NaN115.0NaN2.24.14.10NaNNaNNaNNaNNaNNaNNaNNaN3201.00.0-8.1310016

Last rows

PatientIDHRO2SatTempSBPMAPDBPRespEtCO2BaseExcessHCO3FiO2pHPaCO2SaO2ASTBUNAlkalinephosCalciumChlorideCreatinineBilirubin_directGlucoseLactateMagnesiumPhosphatePotassiumBilirubin_totalTroponinIHctHgbPTTWBCFibrinogenPlateletsAgeGenderUnit1Unit2HospAdmTimeICULOSSepsisLabelSepsisHours
1999011999162.092.035.1111.072.044.010.031.0NaNNaNNaNNaNNaNNaNNaN14.0NaN7.50NaN0.79NaN90.0NaN1.6NaN4.1NaNNaN26.38.4NaN6.4NaN96.08100.01.0-66.1310025
1999111999284.086.535.9116.075.056.015.0NaNNaNNaNNaNNaNNaNNaNNaN37.0NaN9.20NaN9.93NaN97.0NaN1.86.64.5NaN0.4130.29.5NaN2.8NaN198.04511.00.0-4.5510041
1999211999378.092.036.0121.081.056.016.0NaNNaNNaNNaNNaNNaNNaNNaN15.0NaN8.30NaN1.01NaN132.0NaN2.02.64.1NaN0.0142.014.9NaN12.3NaN175.0651NaNNaN-3.5310021
1999311999468.093.035.495.068.048.012.026.0NaNNaN0.47.2528.097.2NaN11.0NaN1.02NaN1.03NaN104.02.251.93.63.6NaNNaN30.310.2NaN7.2NaN62.07110.01.0-29.5710042
1999411999554.092.035.4128.088.066.013.0NaNNaNNaNNaNNaNNaNNaNNaN9.0NaN8.80NaN0.81NaN86.0NaN2.03.03.5NaNNaN39.213.1NaN7.0289.0154.07610.01.0-14.9010042
1999511999669.095.035.781.060.043.016.0NaNNaNNaNNaNNaNNaNNaN849.04.0259.08.70NaN0.41NaN135.0NaN1.6NaN3.33.30.0142.713.8NaN12.6NaN238.0840NaNNaN-6.6910048
1999611999744.089.036.397.063.047.014.045.0NaNNaNNaNNaNNaNNaN24.05.0116.09.80NaN0.690.1101.5NaN3.13.13.10.71.0944.015.038.210.0NaN177.0301NaNNaN-0.0210025
1999711999857.088.036.0119.079.055.017.0NaNNaNNaNNaNNaNNaNNaN9.049.068.07.80NaN6.60NaN83.0NaN1.94.14.20.2NaN26.18.0NaN10.7NaN179.06001.00.0-53.6410049
1999811999987.083.037.2128.090.066.017.0NaNNaNNaNNaNNaNNaNNaN33.028.049.08.40NaN0.98NaN108.0NaNNaNNaN3.40.9NaN19.16.5NaN10.0NaN255.08401.00.0-10.7410020
1999912000072.096.036.4110.078.058.015.0NaNNaNNaNNaNNaNNaNNaN18.09.075.08.90NaN0.530.1123.0NaN2.24.03.30.9NaN37.111.629.15.4NaN216.0620NaNNaN0.0010035